Semantic, Cognitive, and Perceptual Computing: Paradigms That Shape Human Experience
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Thursday, May 3, 2018 - 09:30 am
1400 Storey Innovation Center
Prof. Amit P. Sheth
Abstract: While Bill Gates, Stephen Hawking, Elon Musk, Peter Thiel, and others engage in OpenAI discussions of whether or not AI, robots, and machines will replace humans, proponents of human-centric computing continue to extend work in which humans and machines partner in contextualized and personalized processing of multimodal data to derive actionable information.
This talk describes how maturing towards the emerging paradigms of semantic computing (SC), cognitive computing (CC), and perceptual computing (PC) provides a continuum through which to exploit the ever-increasing and growing diversity of data that could enhance people’s daily lives. SC and CC sift through raw data to personalize it according to context and individual users, creating abstractions that move the data closer to what humans can readily understand and apply in decision-making. PC, which interacts with the surrounding environment to collect data that is relevant and useful in understanding the outside world, is characterized by interpretative and exploratory activities that are supported by the use of prior/background knowledge. Using the examples of personalized digital health and a smart city, we will demonstrate how the trio of these computing paradigms form complementary capabilities that will enable the development of the next generation of intelligent systems. For background: http://bit.ly/PCSComputing.
Biography: Prof. Amit Sheth (http://knoesis.org/amit) is an Educator, Researcher, and Entrepreneur. He is the LexisNexis Ohio Eminent Scholar, a Fellow of both IEEE and AAAI, and the executive director of Kno.e.sis-the Ohio Center of Excellence in Knowledge-enabled Computing at Wright State University. Kno.e.sis has ~75 researchers, including 15 faculty and ~60 funded students. In World Wide Web technology, it is placed among the top 10 universities in the world based on its 10-yr impact. He has founded three companies and continues to advise/direct startups in semantics and healthcare; several commercial products and deployed systems have resulted from his research. Taalee/Semagix, founded in 1999 developed the first knowledge driven semantic search product, similar to the one popularized in 2013 by Google’s knowledge graph enhanced semantic search. He is one of the 100 most cited computer scientists (h-index 98). Some of the recent themes he coined/popularized include smart data (2004), citizen sensing (2008), semantic perception (2008), and continuous semantics (2008). His former students are exceptionally successful as academics in research universities, researchers in industry, and successful entrepreneurs; average citations for his first 18 past PhD students exceed 1,800 (http://j.mp/Kimpact).
Thursday, May 3, 9:30 am – 10:30 am
1400 Storey Innovation Center
The Center for Advanced Analytics and Applied Innovation is hosting speakers Mark Parzyngat, Program Director at IBM Blockchain, and Ben Davis, Chief Technology Officer at Moondog Animation Studio. Tech talks will follow the induction ceremony of Upsilon Pi Epsilon, the Computing honor society.
Jim Stritzinger, Director of the Center for Advanced Analytics and Applied Innovation, is organizing this event.
Over the past few months, 18 mobile app ideas have been developed in conjunction with the computer science department. We are hosting a showcase competition for the 18 teams.
Why Attend?

DISSERTATION DEFENSE
Author: Mark Daniels
Advisor: Dr. Csilla Farkas
Abstract
An individual’s healthcare data may be the most private information a person possesses. Current regulations, such as the Health Insurance Portability and Accountability Act of 1996 (HIPAA), safeguard patient data by assigning a sensitivity level to data items. However, this approach is limited when domain knowledge is used to infer additional patient data. In our research, we investigate privacy violations occurring when non-confidential patient data is combined with medical domain ontologies to disclose a patient’s protected health information (PHI).
We developed a framework that detects privacy violations and eliminates undesired inferences. Our inference channel removal process is based on controlling the release of the data items that lead to undesired inferences. These data items are either blocked from release or generalized to eliminate the disclosure of the PHI. We first developed an exhaustive framework to disrupt the undesired inferences, then improved on the methods using a heuristic-based approach. Our privacy model includes traditional security assessments (i.e., HIPAA) as well as considering safety and patient privacy preferences. We developed a graphic user interface that allows patients to control the release of their data. We also visualize the inferred data using the healthcare domain knowledge.
Date: April 11th, 2018
Time: 10:00 am
Place: Meeting room 2267, Innovation Center
DISSERTATION DEFENSE
Author : Xian Wu
Advisor : Dr. Jenay Beer
Abstract
Maintaining health and wellness while aging-in-place independently is crucial for older adults. Telepresence technology can be potentially beneficial for this target population to stay socially connected. However, this technology is not specifically designed for older adults. For this target population to adopt such technology successfully, it is important to ensure that they do not experience usability barriers. This research uses HCI/HRI concepts and technology design principles for older adults to design, develop and test telepresence user interfaces (UI). This addresses the following research questions: 1): What are the essential usability and privacy-enhanced features needed to inform the design and development of a new telepresence UI for aging population? 2): Is the new telepresence UI perceived as more usable and private by older users compared to traditional telepresence UI design?
Thirty older adults aged above 60 in South Carolina and Georgia participated in a within-subjects user-testing with two UIs: 1) a generic UI called Presence designed based on currently available telepresence robots; and 2) a privacy-enhanced usable telepresence UI named InTouch. Participants tested both UIs in a virtual home environment developed in Unity.
Results of this study suggest that older adults perceived InTouch to be more usable and private. This study provides insight on what usability and privacy features are critical for the aging population to use such telepresence technology. By investigating the design of telepresence robots for older users, and applying those findings to design recommendations, the final goal is to improve the ease of use and privacy level of telepresence robots – not only for our target users, but for all users who wish to enhance their social connectedness.
Date : April 3rd , 2018
Time : 10:00 am
Place : Meeting room 2265, Innovation Center